Instructions to use d-matrix/gemma-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use d-matrix/gemma-2b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="d-matrix/gemma-2b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("d-matrix/gemma-2b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("d-matrix/gemma-2b", trust_remote_code=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use d-matrix/gemma-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "d-matrix/gemma-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d-matrix/gemma-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/d-matrix/gemma-2b

SGLang

How to use d-matrix/gemma-2b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "d-matrix/gemma-2b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d-matrix/gemma-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "d-matrix/gemma-2b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d-matrix/gemma-2b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use d-matrix/gemma-2b with Docker Model Runner:
```
docker model run hf.co/d-matrix/gemma-2b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a d-Matrix functional reference of the GEMMA-2B model. The reference provides the following functional configurations:

Configuration	Explanation
`BASELINE`	a reference functionally equivalent to the original model
`BASIC`	all linear algebraic operands quantized to `MXINT8-64`, and all other operations transformed to approximated kernel simulations

Usage

Install d-Matrix Dmx_Compressor first.

pip install dmx_compressor

The following is an example model and its evaluation.

git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .

from dmx.compressor.modeling import DmxModel
import lm_eval

model_args = "pretrained=d-matrix/gemma-2b,trust_remote_code=True"

lm = lm_eval.api.registry.get_model("hf").create_from_arg_string(model_args, {"batch_size": 1})

# Transform the model with DMX
lm._model = DmxModel.from_torch(lm._model)

eval_results = lm_eval.evaluate(lm, lm_eval.tasks.get_task_dict(["wikitext"]))  # Assign desired task, i.e. "wikitext"

Downloads last month: 6

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for d-matrix/gemma-2b

Quantizations

1 model

Evaluation results

perplexity (BASELINE) on Wikitext
self-reported

42.828
perplexity (BASIC) on Wikitext
self-reported

213.395